New research shows that deep learning can use EEG signals to distinguish Alzheimers disease from frontotemporal dementia with high accuracy. By analyzing both the timing and frequency of brain activity, the model uncovered distinct patterns: broader disruption across multiple regions in Alzheimers and more localized frontal and temporal changes in frontotemporal dementia.
New research shows that deep learning can use EEG signals to distinguish Alzheimers disease from frontotemporal dementia with high accuracy. By analyzing both the timing and frequency of brain activity, the model uncovered distinct patterns: broader disruption across multiple regions in Alzheimers and more localized frontal and temporal changes in frontotemporal dementia.
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